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Toronto Stock Exchange (by bug man, uploaded 114 days ago)
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Recent mergers and acquisitions (by Patrick Fonce, uploaded 261 days ago)
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Hong Kong Stock Exchange - historical data (by The trader, uploaded 104 days ago)
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Chande's Range Action Verification Index - RAVI (by The trader, uploaded 142 days ago)
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How far are the close prices from the support line (by The trader, uploaded 93 days ago)
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Historical futures data (by Brian Brown, uploaded 99 days ago)
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Per Symbol Average Output
by The trader, 216 days ago

This rules analyzer metric calculates the average output of each analyzed rule and on a per-symbol basis. The standard average output adds up the outputs generated by all the positions for all securities then divides this value by the number of positions.
The current metric adds up the outputs separately for each symbol and then divides each symbol sum of outputs by the number of positions generated by each one of them. The results are then averaged to calculate a unique metric.

Here is a quick example:
Let us say we have 2 symbols (A and B), where 'A ' generated two positions with the following outputs: 20 and 60, and 'B' generated three positions with the following outputs: -10, 10 and -30.

The default Average output used by QuantShare will do the following calculations. Sum up all the outputs (20 + 60 - 10 + 10 - 30) then divides them by the number of generated positions (2 for the symbol 'A' and 3 for the symbol 'B'). The result of the above calculations is 10, and that is the average output.

The Average output per symbol calculation steps are as follows: Sum up the outputs for each symbol separately (80 for symbol A and -30 for symbol B), then performs the divisions (80/2=40 for symbol A, and -30/3=-10 for symbol B). The results are now averaged (40 - 10 / 2 = 15), and that is the average output per-symbol.

The example is too simplistic that it doesn't show the real benefits of this measure. But in a nutshell, this metrics tells you if the average output result is due to only a few symbols or not. This is the case when the average output is high while the average output per-symbol is low, and this happens when a few symbols generate much more positions, with high output values, than the others symbols, which have lower output values.

NB: output could refer to security return (performance) or to any other custom value.



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